U.S. patent number 7,171,008 [Application Number 10/193,825] was granted by the patent office on 2007-01-30 for reducing noise in audio systems.
This patent grant is currently assigned to MH Acoustics, LLC. Invention is credited to Gary W. Elko.
United States Patent |
7,171,008 |
Elko |
January 30, 2007 |
**Please see images for:
( Certificate of Correction ) ** |
Reducing noise in audio systems
Abstract
Two or more microphones receive acoustic signals and generate
audio signals that are processed to determine what portion of the
audio signals result from (i) incoherence between the audio signals
and/or (ii) audio-signal sources having propagation speeds
different from the acoustic signals. The audio signals are filtered
to reduce that portion of one or more of the audio signals. The
present invention can be used to reduce turbulent wind-noise
resulting from wind or other airjets blowing across the
microphones. Time-dependent phase and amplitude differences between
the microphones can be compensated for based on measurements made
in parallel with routine audio system processing.
Inventors: |
Elko; Gary W. (Summit, NJ) |
Assignee: |
MH Acoustics, LLC (Summit,
NJ)
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Family
ID: |
27668271 |
Appl.
No.: |
10/193,825 |
Filed: |
July 12, 2002 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20030147538 A1 |
Aug 7, 2003 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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60354650 |
Feb 5, 2002 |
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Current U.S.
Class: |
381/92; 381/122;
381/91 |
Current CPC
Class: |
H04R
3/005 (20130101); H04R 25/407 (20130101); H04R
25/405 (20130101); H04R 2410/07 (20130101); H04R
2430/21 (20130101) |
Current International
Class: |
H04R
3/00 (20060101); H04R 1/02 (20060101) |
Field of
Search: |
;381/97,91,92,122,95,317-318,313,320-321,94.1-3,13 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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06303689 |
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Oct 1994 |
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JP |
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2001124621 |
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May 2001 |
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JP |
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WO 95 16259 |
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Jun 1995 |
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WO |
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Primary Examiner: Chin; Vivian
Assistant Examiner: Suthers; Douglas
Attorney, Agent or Firm: Mendelsohn; Steve
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of the filing date of U.S.
provisional application No. 60/354,650, filed on Feb. 2, 2002.
Claims
What is claimed is:
1. A method for processing audio signals generated by two or more
microphones receiving acoustic signals, comprising the steps of:
(a) generating a ratio of sum and difference signal powers for the
audio signals; and (b) filtering at least one of the audio signals
based on the ratio to reduce a portion of the audio signals
resulting from one or more of (i) incoherence between the audio
signals and (ii) one or more audio-signal sources having
propagation speeds different from the acoustic signals.
2. The invention of claim 1, wherein the audio signals are
generated by two microphones, wherein: a first microphone is either
an omnidirectional microphone or a differential microphone; a
second microphone is either an omnidirectional microphone or a
differential microphone the one or more audio-signal sources
comprises turbulent wind blowing across at least one of the two or
more microphones; at least some of the incoherence between the
audio signals results from microphone self-noise; and the method is
implemented by a hearing aid, a cell phone, or a consumer recording
device.
3. The invention of claim 1, wherein step (a) comprises updating
one or more filter parameters used during the filtering of step (b)
based on the sum and difference signal powers.
4. The invention of claim 3, wherein the sum and difference powers
are generated using audio signals from more than two
microphones.
5. The invention of claim 1, wherein the filtering of step (b) is
further based on an idealized ratio of sum and difference signal
powers for an idealized response of the two or more microphones
receiving acoustic signals from a specified direction.
6. The invention of claim 5, wherein the two or more microphones
are positioned along a linear axis, and the specified direction
corresponds to acoustic signals arriving along the axis.
7. The invention of claim 1, wherein steps (a) and (b) are
implemented independently for each of two or more different
frequency sub-bands in the audio signals.
8. An audio system for processing audio signals generated by two or
more microphones receiving acoustic signals, the audio system
comprising: (a) a signal processor configured to generate a ratio
of sum and difference signal powers for the audio signals; and (b)
a filter configured to filter at least one of the audio signals
based on the ratio to reduce a portion of the audio signals
resulting from one or more of (i) incoherence between the audio
signals and (ii) one or more audio-signal sources having
propagation speeds different from the acoustic signals.
9. The invention of claim 8, wherein the audio signals are
generated by two microphones, wherein: the audio system comprises
the two microphones; a first microphone is either an
omnidirectional microphone or a differential microphone; a second
microphone is either an omnidirectional microphone or a
differential microphone; the one or more audio-signal sources
comprises turbulent wind blowing across at least one of the
microphones; at least some of the incoherence between the audio
signals results from microphone self-noise; and the audio system is
part of a hearing aid, a cell phone, or a consumer recording
device.
10. The invention of claim 8, wherein the signal processor is
configured to update one or more filter parameters used by the
filter based on the sum and difference signal powers.
11. The invention of claim 10, wherein the signal processor
generates the sum and difference powers using audio signals from
more than two microphones.
12. The invention of claim 8, wherein the filtering performed by
the filter is further based on an idealized ratio of sum and
difference signal powers for an idealized response of the two or
more microphones receiving acoustic signals from a specified
direction.
13. The invention of claim 12, wherein the two or more microphones
are positioned along a linear axis, and the specified direction
corresponds to acoustic signals arriving along the axis.
14. The invention of claim 8, wherein processing of the signal
processor and the filter is implemented independently for each of
two or more different frequency sub-bands in the audio signals.
15. A consumer device comprising: (a) two or more microphones
configured to receive acoustic signals and to generate audio
signals; (b) a signal processor configured to generate a ratio of
sum and difference signal powers for the audio signals; and (c) a
filter configured to filter at least one of the audio signals based
on the ratio to reduce a portion of the audio signals resulting
from one or more of (i) incoherence between the audio signals and
(ii) one or more audio-signal sources having propagation speeds
different from the acoustic signals.
16. The invention of claim 15, wherein the consumer device is one
of a hearing aid, a cell phone, and a consumer recording
device.
17. The method of claim 1, wherein the audio signals are generated
in response to a sound field by the at least two microphones of an
audio system, and further comprising the steps of: (c) filtering
the audio signals to compensate for a phase difference between the
at least two microphones; (d) generating a revised phase difference
between the at least two microphones based on the audio signals;
and (e) updating, based on the revised phase difference, at least
one calibration parameter used during the filtering of step
(c).
18. The invention of claim 17, wherein step (d) comprises the step
of characterizing diffuseness of the sound field based on the audio
signals, wherein the revised phase difference is generated only
when the characterized diffuseness of the sound field is determined
to be greater than a specified diffuseness threshold level.
19. The invention of claim 18, wherein step (d) comprises the steps
of: (1) generating front and rear power ratios based on the audio
signals; and (2) comparing the front and rear power ratios to
characterize the diffuseness of the sound field.
20. The invention of claim 19, wherein the front and rear power
ratios are generated by treating the at least two microphones as
sensors in a differential microphone having a cardioid
configuration.
21. The invention of claim 18, wherein step (d) comprises the steps
of: (1) generating an integrated coherence function for each of two
different frequency regions; and (2) comparing the integrated
coherence functions for the two different frequency regions to
characterize the diffuseness of the sound field.
22. The invention of claim 17, wherein: the method is implemented
by a hearing aid, a cell phone, or a consumer recording device;
step (c) further comprises the step of filtering the audio signals
to compensate for an amplitude difference between the at least two
microphones; step (d) further comprises the step of generating a
revised amplitude difference between the at least two microphones
based on the audio signals; and step (e) further comprises the step
of updating, based on the revised amplitude difference, at least
one calibration parameter used in the filtering of step (c).
23. The invention of claim 17, wherein step (e) comprises the step
of switching to a single-microphone mode when the revised phase
difference is sufficiently large.
24. The invention of claim 23, wherein step (e) comprises the step
of selecting a microphone having greatest power for the
single-microphone mode.
25. The invention of claim 17, wherein step (e) comprises the step
of generating a message to notify a user of the existence of a
problem when the revised phase difference or an amplitude
difference between the at least two microphones is sufficiently
large.
26. The invention of claim 17, wherein: the revised phase
difference is computed using background processing; step (d)
further comprises the step of determining how much using the
revised phase difference would improve the filtering of step (c);
and the at least one calibration parameter is updated based on the
revised phase difference when doing so improves the filtering of
step (c) by a sufficient amount.
27. The invention of claim 17, wherein: the audio system comprises
more than two microphones; and step (c) comprises the step of
filtering the audio signals from a subset of the microphones to
compensate for the phase difference.
28. The invention of claim 27, wherein the subset corresponds to
microphones having greatest power.
29. The audio system of claim 8, further comprising: a second
filter configured to filter the audio signals generated in response
to a sound field by the at least two microphones to compensate for
a phase difference between the at least two microphones; and the
signal processor is further configured to: (1) generate a revised
phase difference between the at least two microphones based on the
audio signals; and (2) update, based on the revised phase
difference, at least one calibration parameter used by the second
filter.
30. The invention of claim 29, wherein the audio system further
comprises the at least two microphones.
31. The invention of claim 29, wherein the signal processor is
configured to characterize diffuseness of the sound field based on
the audio signals, wherein the revised phase difference is
generated only when the characterized diffuseness of the sound
field is determined to be greater than a specified diffuseness
threshold level.
32. The invention of claim 31, wherein the signal processor is
configured to: (A) generate front and rear power ratios based on
the audio signals; and (B) compare the front and rear power ratios
to characterize the diffuseness of the sound field.
33. The invention of claim 32, wherein the front and rear power
ratios are generated by treating the at least two microphones as
sensors in a differential microphone having a cardioid
configuration.
34. The invention of claim 31, wherein the signal processor is
configured to: (A) generate an integrated coherence function for
each of two different frequency regions; and (B) compare the
integrated coherence functions for the two different frequency
regions to characterize the diffuseness of the sound field.
35. The invention of claim 29, wherein: the apparatus is part of a
hearing aid, a cell phone, or a consumer recording device; the
second filter is further configured to filter the audio signals to
compensate for an amplitude difference between the at least two
microphones; and the signal processor is further configured to: (i)
generate a revised amplitude difference between the at least two
microphones based on the audio signals; and (ii) update, based on
the revised amplitude difference, at least one calibration
parameter used by the second filter.
36. The invention of claim 29, wherein the signal processor is
configured to switch to a single-microphone mode when the revised
phase difference or an amplitude difference between the at least
two microphones is sufficiently large.
37. The invention of claim 36, wherein the signal processor is
configured to select a microphone having greatest power for the
single-microphone mode.
38. The invention of claim 29, wherein the signal processor is
configured to generate a message to notify a user of the existence
of a problem when the revised phase difference is sufficiently
large.
39. The invention of claim 29, wherein: the revised phase
difference is computed using background processing; the signal
processor is further configured to determine how much using the
revised phase difference would improve the second filter; and the
at least one calibration parameter is updated based on the revised
phase difference when doing so improves the second filter by a
sufficient amount.
40. The invention of claim 29, wherein: the audio system comprises
more than two microphones; and the signal processor is configured
to filter the audio signals from a subset of the microphones to
compensate for the phase difference.
41. The invention of claim 40, wherein the subset corresponds to
microphones having greatest power.
42. The consumer device of claim 15, further comprising: a second
filter configured to the filter audio signals generated in response
to a sound field by the at least two microphones to compensate for
a phase difference between the at least two microphones; and the
signal processor is further configured to: (1) generate a revised
phase difference between the at least two microphones based on the
audio signals; and (2) update, based on the revised phase
difference, at least one calibration parameter used by the second
filter.
43. The invention of claim 42, wherein the consumer device is a
hearing aid, a cell phone, or a consumer recording device.
44. The invention of claim 1, wherein: at least some of the portion
results from incoherence between the audio signals; and at least
some of the incoherence results from presence of a diffuse acoustic
noise field.
45. The invention of claim 1, wherein the audio signals are
generated by an omnidirectional microphone and a differential
microphone.
46. The invention of claim 1, wherein the audio signals are
generated by two omnidirectional microphones.
47. The invention of claim 8, wherein: at least some of the portion
results from incoherence between the audio signals; and at least
some of the incoherence results from presence of a diffuse acoustic
noise field.
48. The invention of claim 8, wherein the two or more microphones
comprise an omnidirectional microphone and a differential
microphone.
49. The invention of claim 8, wherein the two or more microphones
comprise two omnidirectional microphones.
50. The invention of claim 1, wherein the method is implemented
without generating any cross correlation or cross power spectrum of
the audio signals.
51. The invention of claim 8, wherein the signal processor and the
filter are adapted to process the audio signals without generating
any cross correlation or cross power spectrum of the audio
signals.
52. The invention of claim 15, wherein the signal processor and the
filter are adapted to process the audio signals without generating
any cross correlation or cross power spectrum of the audio
signals.
53. The invention of claim 1, wherein the filtering is implemented
independent of any cross correlation or cross power spectrum of the
audio signals.
54. The invention of claim 8, wherein the filter is adapted to
operate independent of any cross correlation or cross power
spectrum of the audio signals.
55. The invention of claim 15, wherein the filter is adapted to
operate independent of any cross correlation or cross power
spectrum of the audio signals.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to acoustics, and, in particular, to
techniques for reducing noise, such as wind noise, generated by
turbulent airflow over microphones.
2. Description of the Related Art
For many years, wind-noise sensitivity of microphones has been a
major problem for outdoor recordings. A related problem is the
susceptibility of microphones to the speech jet, i.e., the flow of
air from the talker's mouth. Recording studios typically rely on
special windscreen socks that either cover the microphone or are
placed between the mouth and the microphone. For outdoor recording
situations where wind noise is an issue, microphones are typically
shielded by acoustically transparent foam or thick fuzzy materials.
The purpose of these windscreens is to reduce--or even
eliminate--the airflow over the active microphone element to
reduce--or even eliminate--noise associated with that airflow that
would otherwise appear in the audio signal generated by the
microphone, while allowing the desired acoustic signal to pass
without significant modification to the microphone.
SUMMARY OF THE INVENTION
The present invention is related to signal processing techniques
that attenuate noise, such as turbulent wind-noise, in audio
signals without necessarily relying on the mechanical windscreens
of the prior art. In particular, according to certain embodiments
of the present invention, two or more microphones generate audio
signals that are used to determine the portion of pickup signal
that is due to wind-induced noise. These embodiments exploit the
notion that wind-noise signals are caused by convective airflow
whose speed of propagation is much less than that of the desired
acoustic signals. As a result, the difference in the output powers
of summed and subtracted signals of closely spaced microphones can
be used to estimate the ratio of turbulent convective wind-noise
propagation relative to acoustic propagation. Since convective
turbulence coherence diminishes quickly with distance, subtracted
signals between microphones are of similar power to summed signals.
However, signals propagating at acoustic speeds will result in
relatively large difference in the summed and subtracted signal
powers. This property is utilized to drive a time-varying
suppression filter that is tailored to reduce signals that have
much lower propagation speeds and/or a rapid loss in signal
coherence as a function of distance, e.g., noise resulting from
relatively slow airflow.
According to one embodiment, the present invention is a method and
an audio system for processing audio signals generated by two or
more microphones receiving acoustic signals. A signal processor
determines a portion of the audio signals resulting from one or
more of (i) incoherence between the audio signals and (ii) one or
more audio-signal sources having propagation speeds different from
the acoustic signals. A filter filters at least one of the audio
signals to reduce the determined portion.
According to another embodiment, the present invention is a
consumer device comprising (a) two or more microphones configured
to receive acoustic signals and to generate audio signals; (b) a
signal processor configured to determine a portion of the audio
signals resulting from one or more of (i) incoherence between the
audio signals and (ii) one or more audio-signal sources having
propagation speeds different from the acoustic signals; and (c) a
filter configured to filter at least one of the audio signals to
reduce the determined portion.
According to yet another embodiment, the present invention is a
method and an audio system for processing audio signals generated
in response to a sound field by at least two microphones of an
audio system. A filter filters the audio signals to compensate for
a phase difference between the at least two microphones. A signal
processor (1) generates a revised phase difference between the at
least two microphones based on the audio signals and (2) updates,
based on the revised phase difference, at least one calibration
parameter used by the filter.
In yet another embodiment, the present invention is a consumer
device comprising (a) at least two microphones; (b) a filter
configured to filter audio signals generated in response to a sound
field by the at least two microphones to compensate for a phase
difference between the at least two microphones; and (c) a signal
processor configured to (1) generate a revised phase difference
between the at least two microphones based on the audio signals;
and (2) update, based on the revised phase difference, at least one
calibration parameter used by the filter.
BRIEF DESCRIPTION OF THE DRAWINGS
Other aspects, features, and advantages of the present invention
will become more fully apparent from the following detailed
description, the appended claims, and the accompanying drawings in
which like reference numerals identify similar or identical
elements.
FIG. 1 shows a diagram of a first-order microphone composed of two
zero-order microphones;
FIG. 2 shows a graph of Corcos model coherence as a function of
frequency for 2-cm microphone spacing and a convective speed of 5
m/s;
FIG. 3 shows a graph of the difference-to-sum power ratios for
acoustic and turbulent signals as a function of frequency for 2-cm
microphone spacing and a convective speed of 5 m/s;
FIG. 4 illustrates noise suppression using a single-channel Wiener
filter;
FIG. 5 illustrates a single-input/single-output noise suppression
system that is essentially equivalent to a system having an array
with two closely spaced omnidirectional microphones;
FIG. 6 shows the amount of noise suppression that is applied by the
system of FIG. 5 as a function of coherence between the two
microphone signals;
FIG. 7 shows a graph of the output signal for a single microphone
before and after processing to reject turbulence using propagating
acoustic gain settings;
FIG. 8 shows a graph of the spatial coherence function for a
diffuse propagating acoustic field for 2-cm spaced microphones,
shown compared with the Corcos model coherence of FIG. 2 and for a
single planewave;
FIG. 9 shows a block diagram of an audio system, according to one
embodiment of the present invention;
FIG. 10 shows a block diagram of turbulent wind-noise attenuation
processing using two closely spaced, pressure (omnidirectional)
microphones, according to one implementation of the audio system of
FIG. 9;
FIG. 11 shows a block diagram of turbulent wind-noise attenuation
processing using a directional microphone and a pressure
(omnidirectional) microphone, according to an alternative
implementation of the audio system of FIG. 9;
FIG. 12 shows a block diagram of an audio system having two
omnidirectional microphones, according to an alternative embodiment
of the present invention; and
FIG. 13 shows a flowchart of the processing of the audio system of
FIG. 12, according to one embodiment of the present invention.
DETAILED DESCRIPTION
Differential Microphone Arrays
A differential microphone array is a configuration of two or more
audio transducers or sensors (e.g., microphones) whose audio output
signals are combined to provide one or more array output signals.
As used in this specification, the term "first-order" applies to
any microphone array whose sensitivity is proportional to the first
spatial derivative of the acoustic pressure field. The term
"n.sup.th-order" is used for microphone arrays that have a response
that is proportional to a linear combination of the spatial
derivatives up to and including n. Typically, differential
microphone arrays combine the outputs of closely spaced transducers
in an alternating sign fashion.
Although realizable differential arrays only approximate the true
acoustic pressure differentials, the equations for the
general-order spatial differentials provide significant insight
into the operation of these systems. To begin, the case for an
acoustic planewave propagating with wave vector k is examined. The
acoustic pressure field for the planewave case can be written
according to Equation (1) as follows:
.function..times.e.function..omega..times..times..times..times.
##EQU00001## where P.sub.o is the planewave amplitude, k is the
acoustic wave vector, r is the position vector relative to the
selected origin, and .omega. is the angular frequency of the
planewave. Dropping the time dependence and taking the
n.sup.th-order spatial derivative yields Equation (2) as
follows:
dd.times..function..function..times..times..times..times..times..times..t-
heta..times.e.times..times. ##EQU00002## where .theta. is the angle
between the wavevector k and the position vector r,
r=.parallel.r.parallel., and k=.parallel.k.parallel.=2.pi./.lamda.,
where .lamda. is the acoustic wavelength. The planewave solution is
valid for the response to sources that are "far" from the
microphone array, where "far" means distances that are many times
the square of the relevant source dimension divided by the acoustic
wavelength. The frequency response of a differential microphone is
a high-pass system with a slope of 6n dB per octave. In general, to
realize an array that is sensitive to the n.sup.th derivative of
the incident acoustic pressure field, m p.sup.th-order transducers
are required, where, m+p-1=n. For example, a first-order
differential microphone requires two zero-order sensors (e.g., two
pressure-sensing microphones).
For a planewave with amplitude P.sub.0 and wavenumber k incident on
a two-element differential array, as shown in FIG. 1, the output
can be written according to Equation (3) as follows:
T.sub.1(k,.theta.)=P.sub.o(1-e.sup.-jkd cos.theta.) (3) where d is
the inter-element spacing and the subscript indicates a first-order
differential array. If it is now assumed that the spacing d is much
smaller than the acoustic wavelength, Equation (3) can be rewritten
as Equation (4) as follows: |T.sub.1(k,.theta.)|.apprxeq.P.sub.okd
cos.theta. (4)
The case where a delay is introduced between these two zero-order
sensors is now examined. For a planewave incident on this new
array, the output can be written according to Equation (5) as
follows: T.sub.1(.omega.,.theta.)=P.sub.o(1-e.sup.-j.omega.(r+d
cos.theta./c)) (5) where .tau. is equal to the delay applied to the
signal from one sensor, and the substitution k=.omega./c has been
made, where c is the speed of sound. If a small spacing is again
assumed (kd<<.pi. and .omega..tau.<<.pi.), then
Equation (5) can be written as Equation (6) as follows:
|T.sub.1(.omega.,.theta.)|.apprxeq.P.sub.o.omega.(.tau.+d/c
cos.theta.) (6) One thing to notice about Equation (6) is that the
first-order array has first-order high-pass frequency dependence.
The term in the parentheses in Equation (6) contains the array
directional response.
Since n.sup.th-order differential transducers have responses that
are proportional to the n.sup.th power of the wavenumber, these
transducers are very sensitive to high wavenumber acoustic
propagation. One acoustic field that has high-wavenumber acoustic
propagation is in turbulent fluid flow where the convective
velocity is much less than the speed of sound. As a result,
prior-art differential microphones have typically required careful
shielding to minimize the hypersensitivity to wind turbulence.
Turbulent Wind-Noise Models
The subject of modeling turbulent fluid flow has been an active
area of research for many decades. Most of the research has been in
underwater acoustics for military applications. With the rapid
growth of commercial airline carriers, there has been a great
amount of work related to turbulent flow excitation of aircraft
fuselage components. Due to the complexity of the equations of
motion describing turbulent fluid flow, only rough approximations
and relatively simple statistical models have been suggested to
describe this complex chaotic fluid flow. One model that describes
the coherence of the pressure fluctuations in a turbulent boundary
layer along the plane of flow is described in G. M. Corcos, The
structure of the turbulent pressure field in boundary layer flows,
J. Fluid Mech., 18: pp. 353 378, 1964, the teachings of which are
incorporated herein by reference. Although this model was developed
for turbulent pressure fluctuation over a rigid half-plane, the
simple Corcos model can be used to express the amount of spatial
filtering of the turbulent jet from a talker. Thus, this model is
used to predict the spatial coherence of the pressure-fluctuation
turbulence for both speech jets as well as free-space
turbulence.
The spatial characteristics of the pressure fluctuations can be
expressed by the space-frequency cross-spectrum function G
according to Equation (7) as follows:
.times..function..psi..omega..intg..infin..infin..times..times..function.-
.psi..tau..times.e.times..times..omega..times..times..tau..times..times.d.-
tau. ##EQU00003## where R is the spatial cross-correlation function
between the two microphone signals, .omega. is the angular
frequency, and .psi. is the general displacement variable which is
directly related to the distance between measurement points. The
coherence function .gamma. is defined as the normalized
cross-spectrum by the auto power-spectrum of the two channels
according to Equation (8) as follows:
.gamma..function..omega..times..times..function..omega..times..times..fun-
ction..omega. ##EQU00004## It is known that large-scale components
of the acoustic pressure field lose coherence slowly during the
convection with free-stream velocity U, while the small-scale
components lose coherence in distances proportional to their
wavelengths. Corcos assumed that the stream-wise coherence decays
spatially as a function of the similarity variable
.omega.r/U.sub.c, where U.sub.c is the convective speed and is
typically related to the free-stream velocity U as U.sub.c=0.8U.
The Corcos model can be mathematically stated by Equation (9) as
follows:
.gamma..function..omega..function..alpha..times..times..omega..times..tim-
es. ##EQU00005## where .alpha. is an experimentally determined
decay constant (e.g., .alpha.=0.125), and r is the displacement
(distance) variable. A plot of this function is shown in FIG. 2.
The rapid decay of spatial coherence results in the difference in
powers between the sums and differences of closely-spaced pressure
(zero-order) microphones to be much smaller than for an acoustic
planewave propagating along the microphone array axis. As a result,
it is possible to detect whether the acoustic signals transduced by
the microphones are turbulent-like or propagating acoustic signals
by comparing the sum and difference signal powers. FIG. 3 shows the
difference-to-sum power ratios (i.e., the ratio of the difference
signal power to the sum signal power) for acoustic and turbulent
signals for a pair of omnidirectional microphones spaced at 2 cm in
a convective fluid flow propagating at 5 m/s. It is clearly seen in
this figure that there is a relatively wide difference between the
desired acoustic and turbulent difference-to-sum power ratios. The
ratio difference becomes more pronounced at low frequencies since
the differential microphone output for desired acoustic signals
rolls off at -6 dB/octave, while the predicted, undesired turbulent
component rolls off at a much slower rate.
If sound arrives from off-axis from the microphone array, the
difference-to-sum power ratio becomes even smaller. (It has been
assumed that the coherence decay is similar in directions that are
normal to the flow). The closest the sum and difference powers come
to each other is for acoustic signals propagating along the
microphone axis (e.g., when .theta.=0 in FIG. 1). Therefore, the
power ratio for acoustic signals will be less than or equal to the
power ratio for acoustic signals arriving along the microphone
axis. This limiting approximation is important to the present
invention's detection and resulting suppression of signals that are
identified as turbulent.
Single-Channel Wiener Filter
It was shown in the previous section that one way to detect
turbulent energy flow over a pair of closely-spaced microphones is
to compare the scalar sum and difference signal power levels. In
this section, it is shown how to use the measured power ratio to
suppress the undesired wind-noise energy.
One common technique used in noise reduction for single input
systems is the well-known technique of spectral subtraction. See,
e.g., S. F. Boll, Suppression of acoustic noise in speech using
spectral subtraction, IEEE Trans. Acoust. Signal Proc., vol.
ASSP-27, Apr. 1979, the teachings of which are incorporated herein
by reference. The basic premise of the spectral subtraction
algorithm is to parametrically estimate the optimal Wiener filter
for the desired speech signal. The problem can be formulated by
defining a noise-corrupted speech signal y(n) according to Equation
(10) as follows: y(n)=s(n)+v(n) (10) where s(n) is the desired
signal and vn) is the noise signal.
FIG. 4 illustrates noise suppression using a single-channel Wiener
filter. The optimal filter is a filter that, when convolved with
the noisy signal y(n), yields the closest (in the mean-square
sense) approximation to the desired signal s(n). This can be
represented in equation form according to Equation (11) as follows:
s(n)=h.sub.opt*y(n) (11) where "*" denotes convolution. The optimal
filter that minimizes the mean-square difference between s(n) and
s(n) is the Wiener filter. In the frequency domain, the result is
given by Equation (12) as follows:
.function..omega..function..omega..function..omega. ##EQU00006##
where G.sub.ys(.omega.) is the cross-spectrum between the signals
s(n) and y(n), and G.sub.yy(.omega.) is the auto power-spectrum of
the signal y(n). Since the noise and desired signals are assumed to
be uncorrelated, the result can be rewritten according to Equation
(13) as follows:
.function..omega..times..times..function..omega..times..times..function..-
omega..times..times..function..omega. ##EQU00007## Rewriting
Equation (11) into the frequency domain and substituting terms
yields Equation (14) as follows:
.function..omega..times..times..function..omega..times..times..function..-
omega..times..times..function..omega..times..function..omega.
##EQU00008## This result is the basic equation that is used in most
spectral subtraction schemes. The variations in spectral
subtraction/spectral suppression algorithms are mostly based on how
the estimates of the auto power-spectrums of the signal and noise
are made.
When speech is the desired signal, the standard approach is to use
the transient nature of speech and assume a stationary (or
quasi-stationary) noise background. Typical implementations use
short-time Fourier analysis-and-synthesis techniques to implement
the Wiener filter. See, e.g., E. J. Diethorn, "Subband Noise
Reduction Methods," Acoustic Signal Processing for
Telecommunication, S. L. Gay and J. Benesty, eds., Kluwer Academic
Publishers, Chapter 9, pp. 155 178. Mar. 2000, the teachings of
which are incorporated herein by reference. Since both speech and
turbulent noise excitation are non-stationary processes, one would
have to implement suppression schemes that are capable of tracking
time-varying signals. As such, time-varying filters should be
implemented. In the frequency domain, this can be accomplished by
using short-time Fourier analysis and synthesis or filter-bank
structures.
Multi-Channel Wiener Filter
The previous section discussed the implementation of the
single-channel Wiener filter. However, the use of microphone arrays
allows for the possibility of having multiple channels. A
relatively simple case is a first-order differential microphone
that utilizes two closely-space omnidirectional microphones. This
arrangement can be seen to be essentially equivalent to a
single-input/single-output system as shown in FIG. 5, where the
desired "noise-free" signal is shown as z(n). It is assumed that
the noise signals at both microphones are uncorrelated, and thus
the two noises can be added equivalently as a single noise source.
If the added noise signal is defined as v(n)=v.sub.1(n)+v.sub.2(n),
then the output from the second microphone can be written according
to Equation (15) as follows:
G.sub.p2p2(.omega.)=G.sub.vv(.omega.)+|H(.omega.)|.sup.2G.sub.p1p1(.omega-
.) (15)
From the previous definition of the coherence function, it can be
shown that the output noise spectrum is given by Equation (16) as
follows:
.times..times..function..omega..gamma..function..omega..times..function..-
omega. ##EQU00009## and the coherent output power is given by
Equation (17) as follows:
.function..omega..gamma..function..omega..times..function..omega.
##EQU00010## Thus the signal-to-noise ratio is given by Equation
(18) as follows:
.function..omega..function..omega..function..omega..gamma..function..omeg-
a..gamma..function..omega. ##EQU00011## Using the expression for
the Wiener filter given by Equation (13) suggests a simple
Wiener-type spectral suppression algorithm according to Equation
(19) as follows:
.function..omega..gamma..function..omega. ##EQU00012## FIG. 6 shows
the amount of noise suppression that is applied as a function of
coherence between the two microphone signals.
One major issue with implementing a Wiener noise reduction scheme
as outlined above is that typical acoustic signals are not
stationary random processes. As a result, the estimation of the
coherence function should be done over short time windows so as to
allow tracking of dynamic changes. This problem turns out to be
substantial when dealing with turbulent wind-noise that is
inherently highly non-stationary. Fortunately, there are other ways
to detect incoherent signals between multi-channel microphone
systems with highly non-stationary noise signals. One way that is
effective for wind-noise turbulence, slowly propagating signals,
and microphone self-noise, is described in the next section.
It is straightforward to extend the two-channel results presented
above to any number of channels by the use of partial coherence
functions that provide a measure of the linear dependence between a
collection of inputs and outputs. A multi-channel least-squares
estimator can also be employed for the signals that are linearly
related between the channels.
Wind-Noise Suppression
The goal of turbulent wind-noise suppression is to determine what
frequency components are due to turbulence (noise) and what
components are desired acoustic signal. Combining the results of
the previous sections indicates how to proceed. The noise power
estimation algorithm is based on the difference in the powers of
the sum and difference signals. If these differences are much
smaller than the maximum predicted for acoustic signals (i.e.,
signals propagating along the axis of the microphones), then the
signal may be declared turbulent and used to update the noise
estimation. The gain that is applied can be the Wiener gain as
given by Equations (14) and (19), or a weighting (preferably less
than 1) that can be uniform across frequency. In general, the gain
can be any desired function of frequency.
One possible general weighting function would be to enforce the
difference-to-sum power ratio that would exist for acoustic signals
that are propagating along the axis of the microphones. The
fluctuating acoustic pressure signals traveling along the
microphone axis can be written for both microphones as follows:
p.sub.1(t)=s(t)+v.sub.1(t)+n.sub.1(t)
p.sub.2(t)=s(t-.tau..sub.s)+v.sub.1(t-.tau..sub.v)+n.sub.2(t) (20)
where .tau..sub.s is the delay for the propagating acoustic signal
s(t), .tau..sub.v is the delay for the convective or slow
propagating waves, and n.sub.1(t) and n.sub.2(t) represent
microphone self-noise and/or incoherent turbulent noise at the
microphones. If the signals are represented in the frequency
domain, the power spectrum of the pressure sum
(p.sub.1(t)+p.sub.2(t)) and difference signals
(p.sub.1(t)-p.sub.2(t)) can be written as follows:
.function..omega..times..times..function..omega..times..function..omega..-
times..times..times..times..UPSILON..function..omega..times..gamma..functi-
on..omega..times..times..omega..times..times..times..times..times..UPSILON-
..function..omega..function..gamma..function..omega..function..omega..func-
tion..omega..times..times..function..omega..times..times..function..omega.-
.times..UPSILON..function..omega..times..gamma..times..omega..times..times-
..UPSILON..function..omega..function..gamma..function..omega..function..om-
ega..function..omega. ##EQU00013## The ratio of these factors
(denoted as PR ) gives the expected power ratio of the difference
and sum signals between the microphones as follows:
.function..omega..function..omega..function..omega. ##EQU00014##
where .gamma..sub.c is the turbulence coherence as measured or
predicted by the Corcos or other turbulence model,
.UPSILON.(.omega.) is the RMS power of the turbulent noise, and
N.sub.1 and N.sub.2 represent the RMS power of the independent
noise at the microphones due to sensor self-noise. For turbulent
flow where the convective wave speed is much less than the speed of
sound, the power ratio will be much greater (by approximately the
ratio of propagation speeds) and thereby moves the power ratio to
unity. Also, as discussed earlier, the convective turbulence
spatial correlation function decays rapidly, and this term becomes
dominant when turbulence (or independent sensor self-noise is
present) and thereby moves the power ratio towards unity. For a
purely propagating acoustic signal traveling along the microphone
axis, the power ratio is as follows:
.function..omega..function..omega..times..times..times.
##EQU00015## For general orientation of a single plane-wave where
the angle between the planewave and the microphone axis is
.theta.,
.function..omega..times..theta..function..omega..times..times..times..tim-
es..times..times..theta..times. ##EQU00016## The results shown in
Equations (24) (25) lead to an algorithm for suppression of airflow
turbulence and sensor self-noise. The rapid decay of spatial
coherence or large difference in propagation speeds, results in the
relative powers between the sums and differences of the closely
spaced pressure (zero-order) microphones to be much smaller than
for an acoustic planewave propagating along the microphone array
axis. As a result, it is possible to detect whether the acoustic
signals transduced by the microphones are turbulent-like noise or
propagating acoustic signals by comparing the sum and difference
powers.
FIG. 3 shows the difference-to-sum power ratio for a pair of
omnidirectional microphones spaced at 2 cm in a convective fluid
flow propagating at 5 m/s. It is clearly seen in this figure that
there is a relatively wide difference between the acoustic and
turbulent sum-difference power ratios. The ratio differences become
more pronounced at low frequencies since the differential
microphone rolls off at -6 dB/octave, where the predicted turbulent
component rolls off at a much slower rate.
If sound arrives from off-axis from the microphone array, the ratio
of the difference-to-sum power levels becomes even smaller as shown
in Equation (25). Note that it has been assumed that the coherence
decay is similar in directions that are normal to the flow. The
closest the sum and difference powers come to each other is for
acoustic signals propagating along the microphone axis. Therefore,
if acoustic waves are assumed to be propagating along the
microphone axis, the power ratio for acoustic signals will be less
than or equal to acoustic signals arriving along the microphone
axis. This limiting approximation is the key to preferred
embodiments of the present invention relating to noise detection
and the resulting suppression of signals that are identified as
turbulent and/or noise. The proposed suppression gain SG(.omega.)
can thus be stated as follows: If the measured ratio exceeds that
given by Equation (25), then the output signal power is reduced by
the difference between the measured power ratio and that predicted
by Equation (25). The equation that implements this gain is as
follows:
.function..omega..function..omega..function..omega. ##EQU00017##
where PR.sub.m(.omega.) is the measured sum and difference signal
power ratio.
FIG. 7 shows the signal output of one of the microphone pair
signals before and after applying turbulent noise suppression using
the weighting gain as given in Equation (25). The turbulent noise
signal was generated by softly blowing across the microphone after
saying the phrase "one, two." The reduction in turbulent noise is
greater than 20 dB. The actual suppression was limited to 25 dB
since it was conjectured that this would be reasonable and that
suppression artifacts might be audible if the suppression were too
large. It is easy to see the acoustic signals corresponding to the
words "one" and "two." This allows one to compare the before and
after processing visually in the figure. One reason that the
proposed suppression technique is so effective for flow turbulence
is due to the fact that these signals have large low frequencies
power, a region where PR.sub.a is small.
Another implementation that is directly related to the Wiener
filter solution is to utilize the estimated coherence function
between pairs of microphones to generate a coherence-based gain
function to attenuate turbulent components. As indicated by FIG. 2,
the coherence between microphones decays rapidly for turbulent
boundary layer flow as frequency increases. For a diffuse sound
field (e.g., uncorrelated sound arriving with equal power from all
directions), the spatial coherence function is real and can be
shown to be equal to Equation (27) as follows:
.gamma..function..times..omega..function..omega..times..times..omega..tim-
es..times. ##EQU00018## where r=d is the microphone spacing. The
coherence function for a single propagating planewave is unity over
the entire frequency range. As more uncorrelated planewaves
arriving from different directions are incorporated, the spatial
coherence function converges to the value for the diffuse case as
given in Equation (16). A plot of the diffuse coherence function of
Equation (27) is shown in FIG. 8. For comparison purposes, the
predicted Corcos coherence functions for 5 m/s flow and for a
single planewave are also shown.
As indicated by FIG. 8, there is a relatively large difference in
the coherence values for a propagating sound field and a turbulent
fluid flow (5 m/s for this case). The large difference suggests
that one could weight the resulting spectrum of the microphone
output by either the coherence function itself or some weighted or
processed version of the coherence. Since the coherence for
propagating acoustic waves is essentially unity, this weighting
scheme will pass the desired propagating acoustic signals. For
turbulent propagation, the coherence (or some processed version) is
low and weighting by this function will diminish the system
output.
Wind-Noise Sensitivity in Differential Microphones
As described in the section entitled "Differential Microphone
Arrays," the sensitivity of differential microphones is
proportional to k.sup.n, where |k|=k=.omega./c and n is the order
of the array. For convective turbulence, the speed of the convected
fluid perturbations is much less that the propagation speed for
radiating acoustic signals. For wind noise, the difference between
propagating speeds is typically about two orders of magnitude. As a
result, for convective turbulence and propagating acoustic signals
at the same frequency, the wave-number ratio will differ by about
two orders of magnitude. Since the sensitivity of differential
microphones is proportional to k.sub.n, the output signal power
ratio for turbulent signals will typically be about two orders of
magnitude greater than the power ratio for propagating acoustic
signals for equivalent levels of pressure fluctuation. As described
in the section entitled "Turbulent Wind-Noise Models," the
coherence of the turbulence decays rapidly with distance. Thus, the
difference-to-sum power ratio is even larger than the ratio of the
convective-to-acoustic propagating speeds.
Microphone Calibration
The techniques described above work best when the microphone
elements (i.e., the different transducers) are fairly closely
matched in both amplitude and phase. This matching of microphone
elements is also important in applications that utilize multiple
closely spaced microphones for directional beamforming. Clearly,
one could calibrate the sensors during manufacturing and eliminate
this issue. However, there is the possibility that the microphones
may deviate in sensitivity and phase over time. Thus, a technique
that automatically calibrates the microphone channels is desirable.
In this section, a relatively straightforward algorithm is
proposed. Some of the measures involved in implementing this
algorithm are similar to those involved in the detection of
turbulence or propagating acoustic signals.
The calibration of amplitude differences may be accomplished by
exploiting the knowledge that the microphones are closely spaced
and, as such, will have very similar acoustic pressures at their
diaphragms. This is especially true at low frequencies. See, e.g.,
U.S. Pat. No. 5,515,445, the teachings of which are incorporated
herein by reference. Phase calibration is more difficult. One
technique that would enable phase calibration can be understood by
examining the spatial coherence values for the sum
(omnidirectional) and difference (dipole) signals between closely
spaced microphones. The spatial coherence can be expressed as the
integral (in 2-D or 3-D) of the directional properties of a
microphone pair. See, e.g., G. W. Elko, "Spatial Coherence
Functions for Differential Microphones in Isotropic Noise Fields,"
Microphone Arrays:: Signal Processing Techniques and Applications,
Springer-Verlag, M. Brandstein and D. Ward, Eds., Chapter 4, pp. 61
85, 2001, the teachings of which are incorporated herein by
reference.
If it is assumed that the acoustic field is spatially homogeneous
(i.e., the correlation function is not dependent on the absolute
position of the sensors), and if it is also assumed that the field
is spherically isotropic (i.e., uncorrelated signals from all
directions), the displacement vector r can be replaced with a
scalar variable r which is the spacing between the two measurement
locations. In that case, the cross-spectral density for an
isotropic field is the average cross-spectral density for all
spherical directions .theta., .phi.. Therefore, space-frequency
cross-spectrum function G between the two sensors can be expressed
by Equation (28) as follows:
.function..times..omega..times..function..omega..times..pi..times..intg..-
pi..times..intg..times..pi..times.e.times..times..times..times..times..tim-
es..theta..times..times..times..theta..times..times.d.theta..times..times.-
d.PHI..times..function..omega..times..function..omega..times..times..omega-
..times..times..times..function..omega..times..function.
##EQU00019## where N.sub.o(.omega.) is the power spectral density
at the measurement locations and it has been assumed, without loss
in generality, that the vector r lies along the z-axis. Note that
the isotropic assumption implies that the auto power-spectral
density is the same at each location. The complex spatial coherence
function .gamma. is defined as the normalized cross-spectral
density according to Equation (29) as follows:
.gamma..function..times..omega..function..times..omega..function..omega..-
times..function..omega..times. ##EQU00020## For spherically
isotropic noise and omnidirectional microphones, the spatial
coherence function is given by Equation (30) as follows:
.gamma..function..omega..times..times..times..times..times..times.
##EQU00021##
In general, the spatial coherence function can be determined by
Equation (31) as follows:
.gamma..function..omega..function..function..theta..PHI..omega..times..fu-
nction..theta..PHI..omega..times.e.times..times..function..function..theta-
..PHI..omega..times..times..function..function..theta..PHI..omega..times.
##EQU00022## where E is the expectation operator over all incident
angles, T.sub.1 and T.sub.2 are the directivity functions for the
two directional sensors, and the superscript "*" denotes the
complex conjugate. The vector r is the displacement vector between
the two microphone locations and r=.parallel.r.parallel.. The
angles .theta. and .phi. are the spherical coordinate angles
(.theta. is the angle off the z-axis and .phi. is the angle in the
x-y plane) and it is assumed, without loss in generality, that the
sensors are aligned along the z-axis. In integral form, for
spherically isotropic fields, Equation (31) can be written as
Equation (32) as follows:
.gamma..function..omega..intg..pi..times..intg..times..pi..times..times..-
function..theta..PHI..omega..times..function..theta..PHI..omega..times.e.t-
imes..times..times..times..theta..times..times..times..theta..times..times-
.d.theta..times.d.PHI..times..intg..pi..times..intg..times..pi..times..fun-
ction..theta..PHI..omega..times..times..times..times..theta..times..times.-
d.theta..times.d.PHI..function..intg..pi..times..intg..times..pi..times..f-
unction..theta..PHI..omega..times..times..times..times..theta..times..time-
s.d.theta..times.d.PHI. ##EQU00023##
For the specific case of the pressure sum (omni) and difference
(dipole) signals, Equation (32) reduces to Equation (33) as
follows: .gamma..sub.dipole-omni(r,.omega.)=0 .A-inverted..omega.,
.A-inverted.r (33) Equation (33) restates a well-known result in
room acoustics: that the acoustic particle velocity components and
the pressure are uncorrelated in diffuse sound fields. However, if
a phase error exists between the individual pressure microphones,
then the ideal difference signal dipole pattern will become
distorted, the numerator term in Equation (32) will not integrate
to zero, and the estimated coherence will therefore not be
zero.
As shown in Equation (27), the cross-spectrum for the pressure
signals for a diffuse field is purely real. If there is phase
mismatch between the microphones, then the imaginary part of the
cross-spectrum will be nonzero, where the phase of the
cross-spectrum is equal to the phase mismatch between the
microphones. Thus, one can use the estimated cross-spectrum in a
diffuse (cylindrical or spherical) sound field as an estimate of
the phase mismatch between the individual channels and then correct
for this mismatch. In order to use this concept, the acoustic noise
field should be close to a true diffuse sound field. Although this
may never be strictly true, it is possible to use typical noise
fields that have equivalent acoustic energy propagation from the
front and back of the microphone pair, which also results in a real
cross-spectral density. One way of ascertaining the existence of
this type of noise field is to use the estimated front and rear
acoustic power from forward and rearward facing supercardioid
beampatterns formed by appropriately combining two closely spaced
pressure microphone signals. See, e.g., G. W. Elko,
"Superdirectional Microphone Arrays," Acoustic Signal Processing
for Telecommunication, S. L. Gay and J. Benesty, eds., Kluwer
Academic Publishers, Chapter 10, pp. 181 237, Mar. 2000, the
teachings of which are incorporated herein by reference.
Alternatively, one could use an adaptive differential microphone
system to form directional microphones whose output is
representative of sound propagating from the front and rear of the
microphone pair. See, e.g., G. W. Elko and A-T. Nguyen Pong. "A
steerable and variable first-order differential microphone," In
Proc. 1997 IEEE ICASSP, Apr. 1997, the teachings of which are
incorporated herein by reference.
Finally, the results given in Equation (5) can be used to
explicitly examine the effect of phase error on the difference
signal between a pair of closely spaced pressure microphones. A
change of variables gives the desired result according to Equation
(34) as follows:
T.sub.1(.omega.,.theta.)=P.sub.o(1-e.sup.-j.omega.(.phi.(.omega.)/.omega.-
+d cos.theta./c)), (34) where .phi.(.omega.) is equal to the phase
error between the microphones. The quantity .phi.(.omega.)/.omega.
is usually referred to as the phase delay. If a small spacing is
again assumed (kd<<.pi. and .phi.(.omega.)<<.pi.), then
Equation (34) can be written as Equation (35) as follows:
|T.sub.1(.omega.,.theta.)|.apprxeq.P.sub.o.omega.(.phi.(.omega.)/.omega.+-
d/c cos .theta.) (35) If Equation (35) is squared and integrated
over all angles of incidence in a diffuse field, then the
differential output is minimized when the phase shift (error)
between the microphones is zero. Thus, one can obtain a method to
calibrate a microphone pair by introducing an appropriate phase
function to one microphone channel that cancels the phase error
between the microphones. The algorithm can be an adaptive
algorithm, such as an LMS (Least Mean Square), NLMS (Normalized
LMS), or Least-Squares, that minimizes the output power by
adjusting the phase correction before the differential combination
of the microphone signals in a diffuse sound field. The advantage
of this approach is that only output powers are used and these
quantities are the same as those for amplitude correction as well
as for the turbulent noise detection and suppression described in
previous sections. Applications
FIG. 9 shows a block diagram of an audio system 900, according to
one embodiment of the present invention. Audio system 900 comprises
two or more microphones 902, a signal processor 904, and a noise
filter 906. Audio system 900 processes the audio signals generated
by microphones 902 to attenuate noise resulting, e.g., from
turbulent wind blowing across the microphones. In particular,
signal processor 904 characterizes the linear relationship between
the audio signals received from microphones 902 and generates
control signals for adjusting the time-varying noise (e.g., Weiner)
filter 906, which filters the audio signals from one or both
microphones 902 to reduce the incoherence between those audio
signals. Depending on the particular application, the
noise-suppression filtering could be applied to the audio signal
from only a single microphone 902. Alternatively, filtering could
be applied to each audio signal. In certain beamforming
applications in which the two or more audio signals are linearly
combined to form an acoustic beam, the noise-suppression filtering
could be applied once to the beamformed signal to reduce
computational overhead. As used in this specification, the
coherence between two audio signals refers to the degree to which
the two signals are linearly related, while, analogously, the
incoherence refers to the degree of non-linearity between those two
signals. Depending on the particular application, noise filter 906
may generate one or more output signals 908. The resulting output
signal(s) 908 are then available for further processing, which,
depending on the application, may involve such steps as additional
filtering, beamforming, compression, storage, transmission, and/or
rendering.
FIG. 10 shows a block diagram of turbulent wind-noise attenuation
processing, according to an implementation of audio system 900
having two closely spaced, pressure (omnidirectional) microphones
1002. In the embodiment of FIG. 10, signal processor 904 of FIG. 9
digitizes (A/D) and transforms (FFT) the audio signal from each
omnidirectional microphone (blocks 1004) and then computes sum and
difference powers of the resulting signals (block 1006) to generate
control signals for adjusting noise filter 906 over time. Noise
filter 906 weights desired signals to attenuate high wavenumber
signals (block 1008) and filters (e.g., equalize, IFFT,
overlap-add, and D/A) the weighted signals to generate output
signal(s) 908 (block 1010). Although any suitable frequency-domain
decomposition could be utilized (such as filter-bank, non-uniform
filter-bank, or wavelet decomposition), uniform short-time Fourier
FFT-based analysis, modification, and synthesis via overlap-add are
shown. The overlap-add method is a standard signal processing
technique where short-time Fourier domain signals are transformed
into the time domain and the final output time signal is
reconstructed by overlapping and adding previous block output
signals from overlapped sampled input blocks.
FIG. 11 shows a block diagram of turbulent wind-noise attenuation
processing, according to an alternative implementation of audio
system 900 having a pressure (omnidirectional) microphone 1102 and
a differential microphone 1103. In this implementation, attenuation
of turbulent energy is accomplished by comparing the output of a
fixed, equalized differential microphone 1102 to that of
omnidirectional microphone 1103 (or even another directional
microphone). The processing of FIG. 11 is similar to that of FIG.
10, except that block 1006 of FIG. 10 is replaced by block 1106 of
FIG. 11. Although this implementation may seem different from the
previous use of sum and difference powers, it is essentially
equivalent.
Since the differential microphone effectively uses the pressure
difference or the acoustic particle velocity, the output power is
directly related to the difference signal power from two closely
space pressure microphones. The output power from a single pressure
microphone is essentially the same (aside from a scale factor) as
that of the summation of two closely space pressure microphones. As
a result, an implementation using comparisons of the output powers
of a directional differential microphone and an omnidirectional
pressure microphone is equivalent to the systems described in the
section entitled "Wind Noise Suppression."
FIG. 12 shows a block diagram of an audio system 1200 having two
omnidirectional microphones 1202, according to an alternative
embodiment of the present invention. Like audio system 900 of FIG.
9, audio system 1200 comprises a signal processor 1204 and a
time-varying noise filter 1206, which operate to attenuate, e.g.,
turbulent wind-noise in the audio signals generated by the two
microphones in a manner analogous to the corresponding components
in audio system 900.
In addition to attenuating turbulent wind-noise, audio system 1200
also calibrates and corrects for differences in amplitude and phase
between the two microphones 1202. To achieve this additional
functionality, audio system 1200 comprises amplitude/phase filter
1203, and, in addition to estimating coherence between the audio
signals received from the microphones, signal processor 1204 also
estimates the amplitude and phase differences between the
microphones. In particular, amplitude/phase filter 1203 filters the
audio signals generated by microphones 1202 to correct for
amplitude and phase differences between the microphones, where the
corrected audio signals are then provided to both signal processor
1204 and noise filter 1206. Signal processor 1204 monitors the
calibration of the amplitude and phase differences between
microphones 1202 and, when appropriate, feeds control signals back
to amplitude/phase filter 1203 to update its calibration processing
for subsequent audio signals. The calibration filter can also be
estimated by using adaptive filters such as LMS (Least Mean
Square), NLMS (Normalized LMS), or Least Squares to estimate the
mismatch between the microphones. The adaptive system
identification would only be active when the field was determined
to be diffuse. The adaptive step-size could be controlled by the
estimation as to how diffuse and spectrally broad the sound field
is, since we want to adapt only when the sound field fulfills these
conditions. The adaptive algorithm can be run in the background
using the common technique of "two-path" estimation common to
acoustic echo cancellation. See, e.g., K. Ochiai, T. Araseki, and
T. Ogihara, "Echo canceller with two echo path models," IEEE Trans.
Commun., vol. COM-25, pp. 589 595, Jun. 1977, the teachings of
which are incorporated herein by reference. By running the adaptive
algorithm in the background, it becomes easy to detect a better
estimation of the amplitude and phase mismatch between the
microphones, since we only need compare error powers between the
current calibrated microphone signals and the background
"shadowing" adaptive microphone signals.
FIG. 13 shows a flowchart of the processing of audio system 1200 of
FIG. 12, according to one embodiment of the present invention. In
particular, the input signals from the two omnidirectional
microphones 1202 are sampled (i.e., A/D converted) (step 1302 of
FIG. 13). Based on the specification of block-size window averaging
time constants (step 1304), blocks of the sampled digital audio
signals are buffered, optionally weighted, and fast Fourier
transformed (FFT) (step 1306). The resulting frequency data for one
or both of the audio signals are then corrected for amplitude and
phase differences between the microphones (step 1308).
After this amplitude/phase correction, the input and sum and
difference powers are generated for the two channels as well as the
coherence (i.e., linear relationship) between the channels, for
example, based on Equation (8) (step 1310). Depending on the
implementation, coherence between the channels can be characterized
once for the entire frequency range or independently within
different frequency sub-bands in a filter-bank implementation. In
this latter implementation, the sum and difference powers would be
computed in each sub-band and then appropriate gains would be
applied across the sub-bands to reduce the estimated
turbulence-induced noise. Depending on the implementation, a single
gain could be chosen for each sub-band, or a vector gain could be
applied via a filter on the sub-band signal. In general, it is
preferable to choose the gain suppression that would be appropriate
for the highest frequency covered by the sub-band. That way, the
gain (attenuation) factor will be minimized for the band. This
might result in less-than-maximum suppression, but would typically
provide less suppression distortion.
In this particular implementation, phase calibration is limited to
those periods in which the incoming sound field is sufficiently
diffuse. The diffuseness of the incoming sound field is
characterized by computing the front and rear power ratios using
fixed or adaptive beamforming (step 1312), e.g., by treating the
two omnidirectional microphones as the two sensors of a
differential microphone in a cardioid configuration. If the
difference between the front and rear power ratios is sufficiently
small (step 1314), then the sound field is determined to be
sufficiently diffuse to support characterization of the phase
difference between the two microphones.
Alternatively, the coherence function, e.g., estimated using
Equation (8), can be used to ascertain if the sound field is
sufficiently diffuse. In one implementation, this determination
could be made based on the ratio of the integrated coherence
functions for two different frequency regions. For example, the
coherence function of Equation (8) could be integrated from
frequency f1 to frequency f2 in a relatively low-frequency region
and from frequency f3 to frequency f4 in a relatively
high-frequency region to generate low- and high-frequency
integrated coherence measures, respectively. Note that the two
frequency regions can have equal or non-equal bandwidths, but, if
the bandwidths are not equal, then the integrated coherence
measures should be scaled accordingly. If the ratio of the
high-frequency integrated coherence measure to the low-frequency
integrated coherence measure is less than some specified threshold
value, then the sound field may be said to be sufficiently
diffuse.
In any case, if the sound field is determined to be sufficiently
diffuse, then the relative amplitude and phase of the microphones
is computed (step 1316) and used to update the calibration
correction processing of step 1306 for subsequent data. In
preferred implementations, the calibration update performed during
step 1316 is sufficiently conservative such that only a fraction of
the calculated differences is updated at any given cycle. In
particular implementations, if the phase difference between the
microphones is sufficiently large (i.e., too large to accurately
correct), then the calibration correction processing of step 1306
could be updated to revert to a single-microphone mode, where the
audio signal from one of the microphones (e.g., the microphone with
the least power) is ignored. In addition or alternatively, a
message (e.g., a pre-recorded message) could be generated and
presented to the user to inform the user of the existence of the
problem.
Whether or not the amplitude and phase calibration is updated in
step 1316, processing continues to step 1318 where the
difference-to-sum power ratio (e.g., in each sub-band) is
thresholded to determine whether turbulent wind-noise is present.
In general, if the magnitude of the difference between the sum and
difference powers is less than a specified threshold level, then
turbulent wind-noise is determined to be present. In that case,
based on the specification of input parameters (e.g., suppression,
frequency weighting and limiting) (step 1320), sub-band suppression
is used to reduce (attenuate) the turbulent wind-noise in each
sub-band, e.g., based on Equation (27) (step 1322). In alternative
implementations, step 1318 may be omitted with step 1322 always
implemented to attenuate whatever degree of incoherence exists in
the audio signals. The preferred implementation may depend on the
sensitivity of the application to suppression distortion that
results from the filtering of step 1322. Whether or not turbulent
wind-noise attenuation is performed, processing continues to step
1324 where output signal(s) 1208 of FIG. 12 are generated using
overlap/adding, equalization, and the application of gain.
In one possible implementation, amplitude/phase filter 1203 of FIG.
12 performs steps 1302 1306 of FIG. 13, signal processor 1204
performs steps 1308 1318, and noise filter 1206 performs steps 1320
1324.
Another simple algorithmic procedure to mitigate turbulence would
be to use the detection scheme as described above and switch the
output signal to the pressure or pressure-sum signal output. This
implementation has the advantage that it could be accomplished
without any signal processing other than the detection of the
output power ratio between the sum and difference or pressure and
differential microphone signals. The price one pays for this
simplicity is that the microphone system abandons its
directionality during situations where turbulence is dominant. This
approach could produce a sound output whose sound quality would
modulate as a function of time (assuming turbulence is varying in
time) since the directional gain would change dynamically. However,
the simplicity of such a system might make it attractive in
situations where significant digital signal processing computation
is not practical.
In one possible implementation, the calibration processing of steps
1312 1316 is performed in the background (i.e., off-line), where
the correction processing of step 1306 continues to use a fixed set
of calibration parameters. When the processor determines that the
revised calibration parameters currently generated by the
background calibration processing of step 1316 would make a
significant enough improvement in the correction processing of step
1306, the on-line calibration parameters of step 1306 are
updated.
Conclusions
In preferred embodiments, the present invention is directed to a
technique to detect turbulence in microphone systems having two or
more sensors. The idea utilizes the measured powers of sum and
difference signals between closely spaced pressure or directional
microphones. Since the ratio of the difference and sum signal
powers is quite similar when turbulent air flow is present and
small when desired acoustic signals are present, one can detect
turbulence or high-wavenumber low-speed (relative to propagating
sound) fluid perturbations.
A Wiener filter implementation for turbulence reduction was derived
and other ad hoc schemes described. Another algorithm presented was
related to the Wiener filter approach and was based on the measured
short-time coherence function between microphone pairs. Since the
length scale of turbulence is smaller than typical spacing used in
differential microphones, weighting the output signal by the
estimated coherence function (or some processed version of the
coherence function) will result in a filtered output signal that
has a greatly reduced turbulent signal component. Experimental
results were shown where the reduction of wind noise turbulence was
reduced by more than 20 dB. Some simplified variations using
directional and non-directional microphone outputs were described,
as well as a simple microphone-switching scheme.
Finally, careful calibration is preferably performed for optimal
operation of the turbulence detection schemes presented. Amplitude
calibration can be accomplished by examining the long-time power
outputs from the microphones. A few techniques based on the
assumption of a diffuse sound field or equal front and rear
acoustic energy or the ratio of integrated frequency bands of the
estimated coherence between microphones were proposed for automatic
phase calibration of the microphones.
Although the present invention is described in the context of
systems having two microphones, the present invention can also be
implemented using more than two microphones. Note that, in general,
the microphones may be arranged in any suitable one-, two-, or even
three-dimensional configuration. For instance, the processing could
be done with multiple pairs of microphones that are closely spaced
and the overall weighting could be a weighted and summed version of
the pair-weights as computed in Equation (27). In addition, the
multiple coherence function (reference: Bendat and Piersol,
"Engineering applications of correlation and spectral analysis",
Wiley Interscience, 1993.) could be used to determine the amount of
suppression for more than two inputs. The use of the
difference-to-sum power ratio can also be extended to higher-order
differences. Such a scheme would involve computing higher-order
differences between multiple microphone signals and comparing them
to lower-order differences and zero-order differences (sums). In
general, the maximum order is one less than the total number of
microphones, where the microphones are preferably relatively
closely spaced.
In a system having more than two microphones, audio signals from a
subset of the microphones (e.g., the two microphones having
greatest power) could be selected for filtering to compensate for
phase difference. This would allow the system to continue to
operate even in the event of a complete failure of one (or possibly
more) of the microphones.
The present invention can be implemented for a wide variety of
applications in which noise in audio signals results from air
moving relative to a microphone, including, but certainly not
limited to, hearing aids, cell phones, and consumer recording
devices such as camcorders. Notwithstanding their relatively small
size, individual hearing aids can now be manufactured with two or
more sensors and sufficient digital processing power to
significantly reduce turbulent wind-noise using the present
invention. The present invention can also be implemented for
outdoor-recording applications, where wind-noise has traditionally
been a problem. The present invention will also reduce noise
resulting from the jet produced by a person speaking or singing
into a close-talking microphone.
Although the present invention has been described in the context of
attenuating turbulent wind-noise, the present invention can also be
applied in other application, such as underwater applications,
where turbulence in the water around hydrophones can result in
noise in the audio signals. The invention can also be useful for
removing bending wave vibrations in structures below the
coincidence frequency where the propagating wave speed becomes less
than the speed of sound in the surrounding air or fluid.
Although the calibration processing of the present invention has
been described in the context of audio systems that attenuate
turbulent wind-noise, those skilled in the art will understand that
this calibration estimation and correction can be applied to other
audio systems in which it is required or even just desirable to use
two or more microphones that are matched in amplitude and/or
phase.
The present invention may be implemented as circuit-based
processes, including possible implementation on a single integrated
circuit. As would be apparent to one skilled in the art, various
functions of circuit elements may also be implemented as processing
steps in a software program. Such software may be employed in, for
example, a digital signal processor, micro-controller, or
general-purpose computer.
The present invention can be embodied in the form of methods and
apparatuses for practicing those methods. The present invention can
also be embodied in the form of program code embodied in tangible
media, such as floppy diskettes, CD-ROMs, hard drives, or any other
machine-readable storage medium, wherein, when the program code is
loaded into and executed by a machine, such as a computer, the
machine becomes an apparatus for practicing the invention. The
present invention can also be embodied in the form of program code,
for example, whether stored in a storage medium, loaded into and/or
executed by a machine, or transmitted over some transmission medium
or carrier, such as over electrical wiring or cabling, through
fiber optics, or via electromagnetic radiation, wherein, when the
program code is loaded into and executed by a machine, such as a
computer, the machine becomes an apparatus for practicing the
invention. When implemented on a general-purpose processor, the
program code segments combine with the processor to provide a
unique device that operates analogously to specific logic
circuits.
Unless explicitly stated otherwise, each numerical value and range
should be interpreted as being approximate as if the word "about"
or "approximately" preceded the value of the value or range.
It will be further understood that various changes in the details,
materials, and arrangements of the parts which have been described
and illustrated in order to explain the nature of this invention
may be made by those skilled in the art without departing from the
principle and scope of the invention as expressed in the following
claims. Although the steps in the following method claims, if any,
are recited in a particular sequence with corresponding labeling,
unless the claim recitations otherwise imply a particular sequence
for implementing some or all of those steps, those steps are not
necessarily intended to be limited to being implemented in that
particular sequence.
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